SPECIES - Evolutionary Quant Research

SPECIES is an evolutionary quantitative research platform designed to discover, evaluate, and validate trading strategies as populations rather than optimizing a single strategy in isolation. A research run begins with a configurable population of strategy genomes. Across multiple generations, SPECIES evaluates each candidate using historical market data, calculates deterministic fitness, applies selection pressure, and creates new descendants through bounded mutation and crossover. Every surviving strategy keeps a traceable lineage, making the evolutionary process inspectable rather than a black box. Evolution alone is not enough to qualify a strategy. Provisional champions must pass separate validation, walk-forward, regime-robustness, and qualification gates before they can move toward execution. Qualified strategies can be connected to Alpaca Paper Trading, but deployment never forces an order. Current market signals are evaluated deterministically, and execution remains protected by explicit approval and RiskGuard boundaries. SPECIES also includes an AI research copilot that explains persisted evidence, fitness, validation results, lineage, and experiment outcomes. The AI does not decide fitness, qualification, risk, or execution; those decisions remain owned by deterministic application logic. The goal is to make quantitative strategy discovery evolutionary, reproducible, explainable, and safer to move from research toward paper execution.